Entorhinal cortex is ground zero for Alzheimer?s disease. This is where the early cortical neurofibrillary tangles ? hyperphosphorylated tau ? appear, which ultimately leads to cell death. Once tau pathology exceeds healthy neurons in EC, the progression from healthy aging to dementia becomes inevitable. However, despite the primary role of entorhinal cortex initiating memory impairment, current imaging biomarkers of entorhinal cortex are large and unidimensional surrogates that fail to account for the earliest tau pathology within this critical structure. Certain EC subregions (i.e. ELr) are hit hard by neurofibrillary tangles, even in mild cases and others cave much later. An accurate, histopathologically- validated imaging biomarker of the entorhinal cortex is an essential step towards identifying key mechanisms of AD pathogenesis and developing novel clinical interventions to stop AD progression. The objective of this project is to generate an entorhinal subregions segmentation for FreeSurfer to serve as such a biomarker. This will be histopathologically-defined at a high resolution by multiple criteria and applicable to other in vivo datasets. Currently, no parcellation software segments an entorhinal parcellation and post mortem imaging affords excellent resolution and allows for direct validation of the pathology. Aim 1 is to develop a novel neuroimaging tool that segments the eight EC subregions in FreeSurfer. Aim 2 is to validate the EC subregions in histology in same cases and establish neuronal and pathology profiles. Aim 3 is to apply the EC subregion segmentation tool to in vivo controls, MCI, and AD subjects in existing structural images at 3T and 7T to test against previously described biomarkers. Comparing the new segmentation against existing biomarkers will ensure specificity, sensitivity and reliability in vivo. We will also acquire a novel high resolution 650 m isotropic MRI dataset in healthy in vivo subjects to push forward a superior resolution for clinical research. The aims develop a pathologically validated tool that will provide clinical researchers the ability to relate quantitative imaging with behavioral and clinical measures. Future application to other in vivo cohorts will transform the specific characterization of the progression from healthy aging to dementia, providing both increased accuracy in our ability to detect AD, as well as improved biological understanding of the pathological effects of the disease that will be critical in developing therapeutic interventions.